ENH: better handling of DF -> ChoiceDataset parametrization #303
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Summary of ChangesHello @VincentAuriau, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed! This pull request introduces a significant enhancement to the Highlights
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Code Review
This pull request refactors the creation of a ChoiceDataset from a wide DataFrame by replacing prefix/suffix-based parameters with a more flexible pattern-based approach. This is a good enhancement that simplifies the API. My review identifies a few critical issues, including a potential NameError due to an undefined variable and a TypeError from an extra parameter being passed. I've also suggested improvements to input validation by using ValueError instead of assert.
choice_learn/data/choice_dataset.py
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| if items_features_patterns is not None: | ||
| assert all(["*" in pattern for pattern in items_features_patterns]), ( | ||
| "items_features_patterns should all contain '*' character." | ||
| ) |
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The variable items_features_names is not defined within the if items_features_patterns is not None: block. This will cause a NameError at line 981 when ChoiceDataset is constructed. This appears to be a regression from the previous implementation which correctly set the feature names. You should derive items_features_names from items_features_patterns.
For example, you could add the following line before this one:
items_features_names = [p.strip("*_.") for p in items_features_patterns]
choice_learn/datasets/base.py
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| items_id=items, | ||
| shared_features_columns=shared_features_by_choice_names, | ||
| items_features_suffixes=items_features_by_choice_names, | ||
| items_features_patterns=["*_%s" % s for s in items_features_by_choice_names], | ||
| delimiter="_", |
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The delimiter parameter is no longer accepted by from_single_wide_df after your changes. This will cause a TypeError. Please remove it.
| items_id=items, | |
| shared_features_columns=shared_features_by_choice_names, | |
| items_features_suffixes=items_features_by_choice_names, | |
| items_features_patterns=["*_%s" % s for s in items_features_by_choice_names], | |
| delimiter="_", | |
| items_features_patterns=["*_%s" % s for s in items_features_by_choice_names], | |
| choices_column=choice_column, | |
| choice_format="items_index", |
choice_learn/data/choice_dataset.py
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| assert all(["*" in pattern for pattern in items_features_patterns]), ( | ||
| "items_features_patterns should all contain '*' character." |
There was a problem hiding this comment.
Using assert for input validation is not recommended as assertions can be disabled (e.g., with Python's -O flag), which would bypass this check. It's better to raise a ValueError for invalid user input.
| assert all(["*" in pattern for pattern in items_features_patterns]), ( | |
| "items_features_patterns should all contain '*' character." | |
| if not all("*" in pattern for pattern in items_features_patterns): | |
| raise ValueError("items_features_patterns should all contain '*' character.") |
choice_learn/data/choice_dataset.py
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| else: | ||
| columns = [f"{item}{delimiter}{available_items_suffix}" for item in items_id] | ||
| available_items_by_choice = df[columns].to_numpy() | ||
| elif available_items_prefix is not None: | ||
| if isinstance(available_items_prefix, list): | ||
| if not len(available_items_prefix) == len(items_id): | ||
| raise ValueError( | ||
| "You have given a list of columns for availabilities." | ||
| "We consider that it is one for each item however lenghts do not match" | ||
| ) | ||
| logging.info("You have given a list of columns for availabilities.") | ||
| logging.info("Each column will be matched to an item, given their order") | ||
| available_items_by_choice = df[available_items_prefix].to_numpy() | ||
| else: | ||
| columns = [f"{available_items_prefix}{delimiter}{item}" for item in items_id] | ||
| assert "*" in available_items_pattern, ( |
There was a problem hiding this comment.
Using assert for input validation is not recommended as assertions can be disabled (e.g., with Python's -O flag). It's better to raise a ValueError for invalid user input.
| else: | |
| columns = [f"{item}{delimiter}{available_items_suffix}" for item in items_id] | |
| available_items_by_choice = df[columns].to_numpy() | |
| elif available_items_prefix is not None: | |
| if isinstance(available_items_prefix, list): | |
| if not len(available_items_prefix) == len(items_id): | |
| raise ValueError( | |
| "You have given a list of columns for availabilities." | |
| "We consider that it is one for each item however lenghts do not match" | |
| ) | |
| logging.info("You have given a list of columns for availabilities.") | |
| logging.info("Each column will be matched to an item, given their order") | |
| available_items_by_choice = df[available_items_prefix].to_numpy() | |
| else: | |
| columns = [f"{available_items_prefix}{delimiter}{item}" for item in items_id] | |
| assert "*" in available_items_pattern, ( | |
| if "*" not in available_items_pattern: | |
| raise ValueError("available_items_pattern should contain '*' character.") |
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